The integration of technological advances into an intelligent grid optimizes the energy system. The dataset provided by smart metering devices allows us to capture and transmit half-hourly electricity consumption profiles, events of poor quality of electricity, tampering attempts, consumption parameters and service events. Unfortunately, the problem of unaccounted consumption has remained relevant even with the use of smart meters. Moreover, today non-technical losses, as a consequence of intentional fraud consumption, are one of the main and very real problems for electric power (EP) distribution companies. There can be no doubt that electricity theft is a problem that affects the efficiency and profitability of power companies. However, the use of technologies can solve such problems. To solve this problem, this article suggests several solutions for classifying time series in order to detect anomalies in electricity consumption. The paper tried to prove the hypothesis about the applicability of artificial intelligence technologies based on neural network training for analyzing data on electricity consumption by consumers. The goal of our research was to develop our own neural network model for enhancing electricity theft. As a result of our work, we concluded that a convolutional neural network (CNN) based on time series classification is an effective tool for finding and combating unaccounted consumption. These results highlight the potential of the proposed method for practical applications in the electricity market, as it can provide reliable and timely information for energy management. The theoretical significance of the study lies in the fact that the work attempts to use machine learning methods to improve theft-detection accuracy. AcknowledgmentsThe work was supported by a grant from the Tatarstan Academy of Science, provided to higher educational institutions, scientific and other organizations for the development of human resources, specifically to encourage their academic and research staff to defend doctoral dissertations and carry out research projects.
User ratings and reviews on marketplaces are subject to systematic distortions, creating serious risks for e-commerce participants and reducing the efficiency of market mechanisms. This study presents a comprehensive analysis of information distortion problems, covering the process from rating formation to its systematic accounting. The aim of the work is to systematize factors of information distortion on marketplaces and develop metrics for quantitative assessment of reliability. An interdisciplinary approach is applied, integrating economic theories, psychological concepts, as well as knowledge from behavioral economics and computer science. The research expands our understanding of information economics in the context of digital platforms, revealing relationships between information quality and market participant behavior. The results have practical significance for marketplace developers, regulators, and users, providing a foundation for creating effective information quality control mechanisms.
Prior research has failed to document an integrated model from UTAUT2 and diffusion innovation theory to explain the intention of fintech adoption among culinary micro, small, and medium enterprises (MSMEs) post-COVID-19. This study investigates the factors affecting the fintech adoption intentions of MSMEs and determines the role of fintech knowledge as a moderating variable. Two hundred ninety one (291) culinary MSMEs from four locations in West Sumatra, Indonesia, participated in this survey. In addition, the SEM-PLS is employed to analyse the significant factors influencing fintech adoption among culinary MSMEs. Model evaluation is conducted to achieve a good-fitting model. The results show five direct relationships between factors and fintech adoption intentions: effort expectancy, social influence, facility condition, price value, and fintech knowledge. In addition, fintech knowledge moderates the relationship between performance expectancy and fintech adoption intentions. In this case, fintech knowledge strengthens the association between performance expectancy and fintech adoption intentions. Thus, fintech knowledge also plays as a moderating variable between effort expectancy and fintech adoption intentions. The finding is relevant to the government program aimed at increasing the fintech knowledge of culinary MSMEs. Fintech companies can improve the expectancy effort, facility condition, social influence, and price value to boost the intention to adopt Fintech. Theoretically, this study contributes to the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and diffusion of innovation theory. Acknowledgements Our paper is funded by the Ministry of Education, Culture, Research and Technology, Republic of Indonesia (no. 186/E5/PG.02.00.PT/2023). We acknowledge the Directorate of Higher Education, the Ministry of Education, Culture, Research and Technology, Republic of Indonesia, for research funding. We would also like to extend our gratitude to the Rector of Universitas Ekasakti and Universitas Bung Hatta, as well as the anonymous reviewers of this article, for their constructive suggestions.
Modern conditions of functioning of social and economic systems, such as increasing political turbulence, increasing transformations of the world economic and financial systems, the growth of the use of digital technologies and artificial intelligence technologies, put forward special requirements for the quality of adaptive properties of economic agents in order to succeed in adapting to changing conditions. In this regard, there is a need to develop various methods and models for assessing the success of adaptation of economic agents to radically changing conditions of their functioning, which determines the relevance of this study. The purpose of this study is to develop the main features of assessing the success of adaptation of economic agents to changing conditions of their functioning and a game-theoretic scheme for assessing success. The proposed model for assessing the success of adaptation of economic entities is based on the construction of a matrix characterizing the dynamics of change in the selected indicator of economic efficiency of functioning of several economic agents, which are the same type of economic entities. The matrix we constructed allows us to perform a comparative analysis of the success of adaptation of economic agents to changing conditions of functioning. This comparative analysis can be performed on the basis of solving the corresponding antagonistic (matrix) game. The proposed method is a new method in the theory and practice of assessing the success of adaptation of economic agents.
Modern IT and AI solutions open new opportunities for the digitalization of economic mechanisms, which leads to increased efficiency and contributes to business digital transformation processes. This article presents a methodological approach based on design principles from the “Institutional Analysis and Development” framework, which allows for the design of information processes underlying the coordination and governance functions of economic mechanisms. On this basis, an initial version of a concept for systemic digitalization of economic mechanisms has been developed, including a description of a multi-agent platform in the spirit of massively multiplayer online games, which ensures data collection and integration, analytical modeling, and support for coordination and decision-making in complex organizational-economic systems. The proposed approach to systemic digitalization of mechanisms based on IT/AI solutions represents a development of business informatics methods for optimizing business processes, for designing information systems to support managerial decision-making and digital transformation of companies. Overall for the economy, this promises an increase in the marginal productivity of business process participants, reduction of their losses, enhancement of business adaptive capabilities, and, as a result, an increase in the average efficiency of the economy. Based on the proposed concept, a “social order” for the IT/AI industry has been formed, which defines key needed solutions for mechanisms digitalization with expected high positive socio-economic effect.
